Low cost torque estimator for DC servo motors

Farnaz, A.H. Z; Sajith, H. S.; Binduhewa, Prabath; Ekanayake, M. P. B.; Samaranayake, B.G.L.T.

doi:10.1109/iciinfs.2015.7399008

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…However, this linear model neglects non-linear terms including friction in the gearbox and bearings as well as motor drivers non-linearity, which leads to reduced accuracy of the model. In [26], the torque was estimated using a detailed motor model that is parametrized using manual system identification. In our work, we follow a data-driven approach for learning a model that allows torque estimation.…”

Section: Torque Estimationmentioning

confidence: 99%

The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks

Klas

Hundhausen

Gao

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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We introduce a multi-functional robotic gripper equipped with a set of actions required for disassembly of electromechanical devices. The gripper consists of a robot arm with 5 degrees of freedom (DoF) for manipulation and a jaw gripper with a 1-DoF rotation joint and a 1-DoF closing joint. The system enables manipulation in 7 DoF and offers the ability to reposition objects in hand and to perform tasks that usually require bimanual systems. The sensor system of the gripper includes relative and absolute joint encoders, force and pressure sensors to provide feedback about interaction forces, a toolmounted camera for screw detection and precise placement of the tool tip using image-based visual servoing. We present a data-driven method for estimating joint torques based on the output voltage and motor speed. Further, we provide methods for teaching disassembly actions based on human demonstration, their representation as movement primitives and execution based on sensory feedback. We provide quantitative results regarding positioning and torque estimation accuracy, disassembly success rate and qualitative results regarding the successful disassembly of hard disc drives.

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Section: Torque Estimationmentioning

confidence: 99%

The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks

Klas

Hundhausen

Gao

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…The electromechanical dynamics of DC motors were studied for system modeling and control in [ 14 ]. In [ 15 ], a Kalman filter was designed for torque estimation based on a dynamic model of low-cost DC motors. The framework of a virtual torque sensor was implemented and validated for industrial robots [ 16 , 17 ], rehabilitation robots [ 18 ], and series elastic actuators in [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Torque Sensor for Low-Cost RC Servo Motors Based on Dynamic System Identification Utilizing Parametric Constraints

Hwang

Minami

Ishikawa

2018

Sensors

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We propose a novel virtual torque sensor for commercial low-cost radio-controlled (RC) servo motors. The virtual torque sensor has played an important role for conventional robots. It has been used for torque-required control applications such as human–robot interaction and under-actuated robots. However, most virtual torque sensors are based on the inversion of actuators or robot dynamics with the assumption that entire dynamics are known. This is not applicable to the RC servo motors that have unknown control structures. As RC servo motors enable researchers and hobbyists to create lightweight but high performance robots in an easy and cost-effective manner, the development of a virtual torque sensor for these motors is necessary. In this study, we propose a design method of a virtual torque sensor for RC servo motors. First, the virtual sensor is derived mathematically based on internal dynamic models with parametric constraints and compared to the conventional model. Second, a dedicated system identification method is developed for the proposed virtual sensor to implement the sensor in actual experiments. Finally, we compare experimental results with the measurements obtained by an actual sensor.

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